Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory72.3 B

Variable types

Categorical1
Numeric7

Alerts

저수위(m) is highly overall correlated with 댐이름High correlation
강우량(mm) is highly overall correlated with 유입량(ms)High correlation
유입량(ms) is highly overall correlated with 강우량(mm) and 4 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
저수율 is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
댐이름 is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
강우량(mm) has 63 (63.0%) zerosZeros
유입량(ms) has 28 (28.0%) zerosZeros
방류량(ms) has 28 (28.0%) zerosZeros
저수량(백만m3) has 28 (28.0%) zerosZeros
저수율 has 28 (28.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:55:25.620163
Analysis finished2023-12-10 10:55:36.148777
Duration10.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강천보
28 
공주보
28 
구담보
28 
구미보
16 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강천보
2nd row강천보
3rd row강천보
4th row강천보
5th row강천보

Common Values

ValueCountFrequency (%)
강천보 28
28.0%
공주보 28
28.0%
구담보 28
28.0%
구미보 16
16.0%

Length

2023-12-10T19:55:36.261735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:55:36.454904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강천보 28
28.0%
공주보 28
28.0%
구담보 28
28.0%
구미보 16
16.0%

일자/시간(t)
Real number (ℝ)

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190214
Minimum20190201
Maximum20190228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:36.681230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190201
5-th percentile20190202
Q120190207
median20190213
Q320190220
95-th percentile20190227
Maximum20190228
Range27
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.980304
Coefficient of variation (CV)3.9525605 × 10-7
Kurtosis-1.1135147
Mean20190214
Median Absolute Deviation (MAD)7
Skewness0.17584102
Sum2.0190214 × 109
Variance63.685253
MonotonicityNot monotonic
2023-12-10T19:55:36.945176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20190201 4
 
4.0%
20190210 4
 
4.0%
20190216 4
 
4.0%
20190202 4
 
4.0%
20190214 4
 
4.0%
20190213 4
 
4.0%
20190212 4
 
4.0%
20190211 4
 
4.0%
20190215 4
 
4.0%
20190209 4
 
4.0%
Other values (18) 60
60.0%
ValueCountFrequency (%)
20190201 4
4.0%
20190202 4
4.0%
20190203 4
4.0%
20190204 4
4.0%
20190205 4
4.0%
20190206 4
4.0%
20190207 4
4.0%
20190208 4
4.0%
20190209 4
4.0%
20190210 4
4.0%
ValueCountFrequency (%)
20190228 3
3.0%
20190227 3
3.0%
20190226 3
3.0%
20190225 3
3.0%
20190224 3
3.0%
20190223 3
3.0%
20190222 3
3.0%
20190221 3
3.0%
20190220 3
3.0%
20190219 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.0485
Minimum4.29
Maximum62.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:37.203380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.29
5-th percentile4.3
Q14.33
median38.03
Q362.43
95-th percentile62.4705
Maximum62.49
Range58.2
Interquartile range (IQR)58.1

Descriptive statistics

Standard deviation22.067328
Coefficient of variation (CV)0.64811455
Kurtosis-1.2757713
Mean34.0485
Median Absolute Deviation (MAD)24.405
Skewness-0.090803112
Sum3404.85
Variance486.96698
MonotonicityNot monotonic
2023-12-10T19:55:37.773304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
4.31 11
 
11.0%
62.47 11
 
11.0%
38.03 10
 
10.0%
38.15 6
 
6.0%
4.3 6
 
6.0%
4.33 5
 
5.0%
62.48 4
 
4.0%
62.43 4
 
4.0%
4.32 3
 
3.0%
38.14 3
 
3.0%
Other values (28) 37
37.0%
ValueCountFrequency (%)
4.29 2
 
2.0%
4.3 6
6.0%
4.31 11
11.0%
4.32 3
 
3.0%
4.33 5
5.0%
4.34 1
 
1.0%
25.58 1
 
1.0%
26.5 1
 
1.0%
27.37 1
 
1.0%
28.37 1
 
1.0%
ValueCountFrequency (%)
62.49 1
 
1.0%
62.48 4
 
4.0%
62.47 11
11.0%
62.46 1
 
1.0%
62.45 3
 
3.0%
62.44 2
 
2.0%
62.43 4
 
4.0%
62.42 2
 
2.0%
38.18 1
 
1.0%
38.17 1
 
1.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.742418
Minimum0
Maximum22.2146
Zeros63
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:38.058716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.020275
95-th percentile1.40068
Maximum22.2146
Range22.2146
Interquartile range (IQR)0.020275

Descriptive statistics

Standard deviation3.2829931
Coefficient of variation (CV)4.4220279
Kurtosis25.721657
Mean0.742418
Median Absolute Deviation (MAD)0
Skewness4.9831028
Sum74.2418
Variance10.778044
MonotonicityNot monotonic
2023-12-10T19:55:38.315543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.0 63
63.0%
0.0119 3
 
3.0%
0.0595 1
 
1.0%
0.0607 1
 
1.0%
0.4162 1
 
1.0%
0.0014 1
 
1.0%
12.1577 1
 
1.0%
0.0511 1
 
1.0%
0.0201 1
 
1.0%
0.0059 1
 
1.0%
Other values (26) 26
26.0%
ValueCountFrequency (%)
0.0 63
63.0%
0.0014 1
 
1.0%
0.0054 1
 
1.0%
0.0059 1
 
1.0%
0.0072 1
 
1.0%
0.0114 1
 
1.0%
0.0119 3
 
3.0%
0.013 1
 
1.0%
0.0182 1
 
1.0%
0.0192 1
 
1.0%
ValueCountFrequency (%)
22.2146 1
1.0%
16.7901 1
1.0%
12.1577 1
1.0%
11.4173 1
1.0%
8.1491 1
1.0%
1.0455 1
1.0%
0.8994 1
1.0%
0.4162 1
1.0%
0.2156 1
1.0%
0.1153 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.78372
Minimum0
Maximum125.669
Zeros28
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:38.637838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median39.3165
Q3107.699
95-th percentile123.11825
Maximum125.669
Range125.669
Interquartile range (IQR)107.699

Descriptive statistics

Standard deviation45.495436
Coefficient of variation (CV)0.86192175
Kurtosis-1.3290716
Mean52.78372
Median Absolute Deviation (MAD)39.3165
Skewness0.34165243
Sum5278.372
Variance2069.8347
MonotonicityNot monotonic
2023-12-10T19:55:38.922936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 28
28.0%
35.663 1
 
1.0%
34.017 1
 
1.0%
35.144 1
 
1.0%
35.424 1
 
1.0%
34.736 1
 
1.0%
35.932 1
 
1.0%
41.553 1
 
1.0%
49.048 1
 
1.0%
48.585 1
 
1.0%
Other values (63) 63
63.0%
ValueCountFrequency (%)
0.0 28
28.0%
18.923 1
 
1.0%
18.959 1
 
1.0%
32.765 1
 
1.0%
33.164 1
 
1.0%
33.469 1
 
1.0%
33.573 1
 
1.0%
33.747 1
 
1.0%
33.851 1
 
1.0%
33.961 1
 
1.0%
ValueCountFrequency (%)
125.669 1
1.0%
125.668 1
1.0%
125.179 1
1.0%
124.545 1
1.0%
123.902 1
1.0%
123.077 1
1.0%
122.869 1
1.0%
121.481 1
1.0%
120.593 1
1.0%
120.006 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.08987
Minimum0
Maximum159.925
Zeros28
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:39.205745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median42.5485
Q3108.969
95-th percentile123.9277
Maximum159.925
Range159.925
Interquartile range (IQR)108.969

Descriptive statistics

Standard deviation47.236404
Coefficient of variation (CV)0.84215571
Kurtosis-1.3516432
Mean56.08987
Median Absolute Deviation (MAD)42.5485
Skewness0.2596396
Sum5608.987
Variance2231.2779
MonotonicityNot monotonic
2023-12-10T19:55:39.625905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 28
28.0%
35.584 1
 
1.0%
34.096 1
 
1.0%
35.065 1
 
1.0%
35.424 1
 
1.0%
34.657 1
 
1.0%
35.932 1
 
1.0%
41.789 1
 
1.0%
49.127 1
 
1.0%
48.27 1
 
1.0%
Other values (63) 63
63.0%
ValueCountFrequency (%)
0.0 28
28.0%
32.607 1
 
1.0%
33.4 1
 
1.0%
33.615 1
 
1.0%
33.627 1
 
1.0%
33.646 1
 
1.0%
33.668 1
 
1.0%
33.888 1
 
1.0%
34.028 1
 
1.0%
34.096 1
 
1.0%
ValueCountFrequency (%)
159.925 1
1.0%
125.669 1
1.0%
125.668 1
1.0%
124.545 1
1.0%
124.131 1
1.0%
123.917 1
1.0%
123.077 1
1.0%
122.523 1
1.0%
121.972 1
1.0%
120.957 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.18054
Minimum0
Maximum38.688
Zeros28
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:39.900021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.4465
Q39.37325
95-th percentile38.35675
Maximum38.688
Range38.688
Interquartile range (IQR)9.37325

Descriptive statistics

Standard deviation11.196717
Coefficient of variation (CV)1.3687015
Kurtosis2.1404433
Mean8.18054
Median Absolute Deviation (MAD)2.4465
Skewness1.7951975
Sum818.054
Variance125.36647
MonotonicityNot monotonic
2023-12-10T19:55:40.136540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.0 28
28.0%
2.436 11
 
11.0%
8.864 10
 
10.0%
9.407 6
 
6.0%
2.429 6
 
6.0%
2.45 5
 
5.0%
2.443 3
 
3.0%
38.485 3
 
3.0%
9.362 3
 
3.0%
38.688 2
 
2.0%
Other values (21) 23
23.0%
ValueCountFrequency (%)
0.0 28
28.0%
2.422 2
 
2.0%
2.429 6
 
6.0%
2.436 11
 
11.0%
2.443 3
 
3.0%
2.45 5
 
5.0%
2.457 1
 
1.0%
8.697 1
 
1.0%
8.773 1
 
1.0%
8.819 2
 
2.0%
ValueCountFrequency (%)
38.688 2
2.0%
38.485 3
3.0%
38.35 1
 
1.0%
36.928 1
 
1.0%
35.031 1
 
1.0%
33.447 1
 
1.0%
31.931 1
 
1.0%
30.35 1
 
1.0%
28.374 1
 
1.0%
24.329 1
 
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.969
Minimum0
Maximum109.3
Zeros28
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:40.388560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.75
Q3101
95-th percentile107.8
Maximum109.3
Range109.3
Interquartile range (IQR)101

Descriptive statistics

Standard deviation43.407209
Coefficient of variation (CV)1.0101983
Kurtosis-1.525416
Mean42.969
Median Absolute Deviation (MAD)15.75
Skewness0.49177988
Sum4296.9
Variance1884.1858
MonotonicityNot monotonic
2023-12-10T19:55:40.627926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 28
28.0%
15.7 14
14.0%
101.6 10
 
10.0%
15.6 8
 
8.0%
107.8 6
 
6.0%
15.8 6
 
6.0%
73.0 3
 
3.0%
107.3 3
 
3.0%
73.4 2
 
2.0%
101.0 2
 
2.0%
Other values (18) 18
18.0%
ValueCountFrequency (%)
0.0 28
28.0%
15.6 8
 
8.0%
15.7 14
14.0%
15.8 6
 
6.0%
20.2 1
 
1.0%
27.2 1
 
1.0%
35.2 1
 
1.0%
46.1 1
 
1.0%
53.8 1
 
1.0%
57.6 1
 
1.0%
ValueCountFrequency (%)
109.3 1
 
1.0%
108.8 1
 
1.0%
108.3 1
 
1.0%
107.8 6
6.0%
107.3 3
 
3.0%
103.1 1
 
1.0%
102.1 1
 
1.0%
101.6 10
10.0%
101.0 2
 
2.0%
100.5 1
 
1.0%

Interactions

2023-12-10T19:55:34.089791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:26.015889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:27.111282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:28.175544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:29.805112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:31.435337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:32.837512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:34.267432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:26.191430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:27.261162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:28.341509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:29.992330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:31.618868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:33.022795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:34.461718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:26.331674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:27.397336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:28.497452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:30.151320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:31.789361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:33.194135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:34.664784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:26.471140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:27.536590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:28.662702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:30.319236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:31.971128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:33.359996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:34.925257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:26.629454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:27.693444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:28.838023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:30.683173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:32.252884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:33.534377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:35.229642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:26.804194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:27.862511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:29.033184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:30.900537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:32.442413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:33.747414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:35.520766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:26.970839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:28.028042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:29.628641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:31.158951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:32.658276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:33.928722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:55:40.854183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.9990.9980.9731.000
일자/시간(t)0.0001.0000.0000.4540.3600.0000.0000.000
저수위(m)1.0000.0001.0000.0001.0000.9140.8840.962
강우량(mm)0.0000.4540.0001.0000.0830.0770.0000.000
유입량(ms)0.9990.3601.0000.0831.0000.9300.9040.939
방류량(ms)0.9980.0000.9140.0770.9301.0000.9420.975
저수량(백만m3)0.9730.0000.8840.0000.9040.9421.0000.995
저수율1.0000.0000.9620.0000.9390.9750.9951.000
2023-12-10T19:55:41.116661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.000-0.013-0.013-0.0180.002-0.273-0.1600.000
저수위(m)-0.0131.000-0.247-0.300-0.309-0.358-0.2400.995
강우량(mm)-0.013-0.2471.0000.5230.4910.3980.4820.000
유입량(ms)-0.018-0.3000.5231.0000.9790.7950.9360.954
방류량(ms)0.002-0.3090.4910.9791.0000.8260.9270.919
저수량(백만m3)-0.273-0.3580.3980.7950.8261.0000.8780.763
저수율-0.160-0.2400.4820.9360.9270.8781.0000.962
댐이름0.0000.9950.0000.9540.9190.7630.9621.000

Missing values

2023-12-10T19:55:35.777861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:55:36.050839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
0강천보2019020138.160.0119111.294111.2949.452108.3
1강천보2019020238.140.0119109.556110.6049.362107.3
2강천보2019020338.1522.2146108.032107.5089.407107.8
3강천보2019020438.180.0212115.61114.0389.543109.3
4강천보2019020538.170.0113.357113.8819.497108.8
5강천보2019020638.150.0111.238112.2869.407107.8
6강천보2019020738.140.1115107.445107.9699.362107.3
7강천보2019020838.150.0110.072109.5489.407107.8
8강천보2019020938.150.0108.776108.7769.407107.8
9강천보2019021038.140.0106.119106.6439.362107.3
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구미보2019020730.30.01380.40298.42936.92870.0
91구미보2019020830.020.067.06689.01235.03166.4
92구미보2019020929.780.059.64477.98733.44763.4
93구미보2019021029.550.061.33678.87331.93160.6
94구미보2019021129.310.052.65270.95230.3557.6
95구미보2019021229.010.060.40383.27828.37453.8
96구미보2019021328.370.066.85113.66724.32946.1
97구미보2019021427.370.092.948159.92518.54235.2
98구미보2019021526.50.018.92367.29214.36327.2
99구미보2019021625.580.011418.95961.92710.65120.2